text: import torch
  import torch.nn.functional as F
  import torch.nn as nn
  
  # Swish
  class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
      @staticmethod
      def forward(x):
          return x * torch.sigmoid(x)
  
  
  class HardSwish(nn.Module):  # https://arxiv.org/pdf/1905.02244.pdf
      @staticmethod
      def forward(x):
          return x * F.hardtanh(x + 3, 0., 6., True) / 6.
  
  
  class Mish(nn.Module):  # https://github.com/digantamisra98/Mish
      @staticmethod
      def forward(x):
          return x * F.softplus(x).tanh()
  
  
  class MemoryEfficientMish(nn.Module):
      class F(torch.autograd.Function):
          @staticmethod
          def forward(ctx, x):
              ctx.save_for_backward(x)
              return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
  
          @staticmethod
          def backward(ctx, grad_output):
              x = ctx.saved_tensors[0]
              sx = torch.sigmoid(x)
              fx = F.softplus(x).tanh()
              return grad_output * (fx + x * sx * (1 - fx * fx))
  
      def forward(self, x):
          return self.F.apply(x)
  
  
  class FReLU(nn.Module):  # FReLU https://arxiv.org/abs/2007.11824
      def __init__(self, c1, k=3):  # ch_in, kernel
          super().__init__()
          self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
          self.bn = nn.BatchNorm2d(c1)
  
      def forward(self, x):
          return torch.max(x, self.bn(self.conv(x)))
  
  
  class AconC(nn.Module):  # ACON https://arxiv.org/pdf/2009.04759.pdf
      r""" ACON activation (activate or not).
      AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
      according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
      """
  
      def __init__(self, c1):
          super().__init__()
          self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
          self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
          self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
  
      def forward(self, x):
          dpx = (self.p1 - self.p2) * x
          return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
  
  
  class MetaAconC(nn.Module):
      r""" ACON activation (activate or not).
      MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
      according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
      """
  
      def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
          super().__init__()
          c2 = max(r, c1 // r)
          self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
          self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
          self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
          self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
          # self.bn1 = nn.BatchNorm2d(c2)
          # self.bn2 = nn.BatchNorm2d(c1)
  
      def forward(self, x):
          y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
          # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
          # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))  # bug/unstable
          beta = torch.sigmoid(self.fc2(self.fc1(y)))  # bug patch BN layers removed
          dpx = (self.p1 - self.p2) * x
          return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
  
  
  